We test four SOTA methods, i.e., IOU [1]}, Tracktor [2]}, MPNTrack [3]} and CenterTrack [4]} on MOT17, combined with the proposed tracklet booster method.
Specifically, the IOU tracker uses intersection over union between bounding boxes across frames as the main clue for data association; Tracktor exploits the bounding box regression of an object detector to predict the position of an object in the next frame, given sequential input frames; MPNTrack exploits the classical network flow formulation of MOT to define a fully differentiable framework based on message passing networks; while CenterTrack applies detection to a pair of frames and associate the objects to the previous frame. The results are reported in Table REF .
For each pair of comparisons, the original method is shown in the first row, while the method with the proposed TBooster (short for “tracklet booster”) is shown in the second row. As expected, there are significant improvements on the IDF1 score, ranging from +0.6% to +5.7%. Since we do not tackle detection algorithms, the MOTA metrics roughly remain the same with TBooster added.
